• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.837-844
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• 2010

Prediction of the prosodic phrase boundary is one of the most important natural language processing tasks. We propose, for the natural prediction of the Korean prosodic phrase boundary, a statistical approach incorporating efficient learning features. These new features reflect the factors that affect generation of the prosodic phrase boundary better than existing learning features. Notably, moreover, such learning features, extracted according to the hand-crafted prosodic phrase boundary prediction rule, impart higher accuracy. We developed a statistical model for Korean prosodic phrase boundaries based on the proposed new features. The results were 86.63% accuracy for three levels (major break, minor break, no break) and 81.14% accuracy for six levels (major break with falling tone/rising tone, minor break with falling tone/rising tone/middle tone, no break).

• Journal of the Korea Computer Industry Society
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• v.2 no.9
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• pp.1175-1182
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• 2001

One to one Marketing (a.k.a. database marketing or relationship marketing) is one of the many fields that will benefit from the electronic revolution and shifts in consumer sales and advertising. As a component of intelligent customer services on Internet storefront, this paper describes technology of providing personalized advertisement using the market basket analysis, a well-Known data mining technique. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customer's data. However, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed Knowledge base. In this paper, using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.7-8
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• 1994

최근 들어 원자재, 재공품 또는 완제품을 신속하고 정확하게 공급/배분하기 위해 저장과 인출을 담당하는 Material Handling System을 이용하여 작업자의 개입요소를 줄이며, 제고관리 Computer를 이용하여 입고/출고 명령을 유효적절하게 처리하는 ASRS(Atomated Storage and Retreival System : 자동창고 시스템)가 널리 공급되고 있다. 중앙은행의 현금창고, 병원의 약품창고, 식품/화장품 회사의 배송창고, 군수물자의 군납창고에 이르기까지 물품의 저장 또는 공급의 필용성을 갖는 곳에서는 어디든지 찾아볼 수 있는 ASRS는 가깝게는 관공소나 대형빌딩의 주차장에도 이의 개념이 도입되어 사용됨을 볼 수 있다. 최근의 인금인상, 구인난등의 이유로 ASRS설치는 계속 증가할 추세에 있으나 자동 창고 시스템을 설치하기 위해서는 막대한 초기 투자가 필요하며 시스템의 설계 및 설치후 운영에 대한 연구가 반드시 필요하다. ASRS의 운영 Rule 검증, 수행능력 분석등의 목적을 갖는 연구에는 여러 접근방법이 있을 수 있으나 구성 설비와 운영 Rule의 복잡한 관계로 컴퓨터 시뮬레이션의 거의 유일한 문제해결 방법이다. ASRS의 Modeling에 관한 기존의 연구로는 수리모델 수립. 이산사건 시스템의 관점에서 event-graphy, petri-net을 이용한 modeling이 있으며 ASRS에 대한 전용 Simulator 개발등이 진행되었다. 본 연구의 대상 시스템은 2개의 Rack과 하나의 Stacker Crane 으로 구성된 Aisle과 입출고의 물류를 처리하는 순환 RGVS(Rail Guided Vehicle System), 입/출고장을 구성하는 Conveyor Net등으로 이루어진 제조-물류시스템의 일반적인 ASRS이다. 또 이 ASRS의 입/출고 방식은 전수 입/출고만을 포함하며 Blocking 방지를 위한 Capaicty 예약, 다중설비 선택등의 문제등을 고려하고 있다. 본 연구의 접근방법으로는 ASRS의 개념적인 Reference Model을 수립하고 이 Reference Model에 대한 Formal Model로 DEVS(Discrete Event System Specification)을 이용하여 시스템을 Modeling하였다. 이의 Computer Simulation을 위하여 DEVS형식론 환경에서의 Simulation Language인 DEVSim ++ⓒ를 이용하여 시스템을 구현하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.62-72
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• 1999

We consider a Direct Input Output Manufacturing System(DIOMS) which has a munber of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval(S/R) machine handles parts placed on pallets for the machine centers located at either one or both sides of the AS/RS. This paper deals with the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the S/R machine, machine center-based part type selection rule and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this paper, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.460-465
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• 2007

In this paper, we propose a novel pattern classification method based on statistical properties of the given data and fuzzy-rough set to minimize the coupling problem of the rules. In the proposed method, statistical properties is used by a selection criteria for deciding a partition number of antecedent fuzzy sets, and for minimizing an coupling problem of the generated rules. Moreover, rough set is used as a tool to remove unnecessary attributes between generated rules from the numerical data. In order to verify the validity of the proposed method, we compared the classification results (i.e, classification precision) of the proposed with the conventional pattern classification methods on the Fisher's IRIS data. From experiment results, we can conclude that the proposed method shows relatively better performance than those of the classification methods based on the conventional approaches.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.53-64
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• 2002

Association rule discovery is a method that detects associative relationships between items or attributes in transactions. It is one of the most widely studied problems in data mining because it offers useful insight into the types of dependencies that exist in a data set. However, most studies on association rule discovery have the drawback that they can not discover association rules among user groups that have common characteristics. To solve this problem, we segment the set of users into user-subgroups by using feature selection and the user segmentation, thus local association rules in user-subgroup can be discovered. To evaluate that the local association rules are more appropriated than the global association rules in each user-subgroup, derived local association rules are compared with global association rules in terms of several evaluation measures.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.18-28
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• 2006

We consider a Direct Input Output Manufacturing System(DIOMS) which has a munber of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval (S/R) machine handles parts placed on pallets for the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the S/R machine, machine center-based part type selection rule, and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this paper, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.1
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• pp.43-55
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• 2009

We consider a Direct Input Output Manufacturing System(DIOMS) which has a number of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval (S/R) machine handles parts placed on pallets for the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the SIR machine, machine center-based part type selection rule, and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this paper, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.133-142
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• 2010

To monitor composition and change of the national land, intelligent topographical classifier which enables accurate classification of land-cover types from airborne LiDAR data is highly required. We developed a topographical classifier development support system cooperating with da1a mining tool WEKA to help users to construct accurate topographical classification systems. The topographical classifier development support system has the following functions; superposing LiDAR data upon corresponding aerial images, dividing LiDAR data into tiles for efficient processing, 3D visualization of partial LiDAR data, feature from tiles, automatic WEKA input generation, and automatic C++ program generation from the classification rule set. In addition, with dam mining tool WEKA, we can choose highly distinguishable features by attribute selection function and choose the best classification model as the result topographical classifier. Therefore, users can easily develop intelligent topographical classifier which is well fitted to the developing objectives by using the topographical classifier development support system.

• Journal of the Ergonomics Society of Korea
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• v.34 no.3
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• pp.199-215
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• 2015

Objective: The purpose of this study is to find the level of physical relaxation of individual subject by monitoring psychophysiological biofeedback to different types of mattresses. And, the study also aims to find a protocol to make a selection of the best mattress based on the measured information. Background: In Korea, there are an increasing number of people using western style bed. However, they are often fastidious in choosing the right mattress for them. In fact, people use their past experience with their old mattress as well as the spontaneous experience they encounter in a show room to finally decide to buy a bed. Method: Total five mattresses were tested in this study. After measuring the elasticity of the mattresses, they were sorted into five different classes. Physiological and psychological variables including Electromyography (EMG), heart rates (HR), oxygen saturations (SaO2) were used. In addition, the peak body pressure concentration rate was used to find uncomfortably pressured body part. Finally, the personal factors and subjective satisfaction were also examined. A protocol was made to select the best mattress for individual subject. The selection rule for the protocol considered all the variables tested in this study. Results: The result revealing psychological comfort range of 0.68 to 0.95, dermal comfort range of 3.15 to 6.07, back muscle relaxation range of 0.25 to 1.64 and personal habit range of 2.0 to 3.4 was drawn in this study. Also a regression model was developed to predict biofeedback with the minimal use of biofeedback devices. Moreover results from the proposed protocol with the regression equation and subjective satisfaction were compared with each other for validation. Ten out of twenty subjects recorded the same level of relaxation, and eight subjects showed one-level difference while two subjects showed two-levels difference. Conclusion: The psychophysiological variables and suitability selection process used in this study seem to be used for selecting and assessing ergonomic products mechanically or emotionally. Application: This regression model can be applied to the mattress industry to estimate back muscle relaxation using dermal, psychophysiology and personal habit values.